New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?
We assess the forecasting performance of the nowcasting model developed at the New York FED. We show that the observation regarding a striking difference in the model’s predictive ability across business cycle phases made earlier in the literature also applies here. During expansions, the nowcasting...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Econometrics |
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| Online Access: | https://www.mdpi.com/2225-1146/9/1/11 |
| _version_ | 1797413014951952384 |
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| author | Boriss Siliverstovs |
| author_facet | Boriss Siliverstovs |
| author_sort | Boriss Siliverstovs |
| collection | DOAJ |
| description | We assess the forecasting performance of the nowcasting model developed at the New York FED. We show that the observation regarding a striking difference in the model’s predictive ability across business cycle phases made earlier in the literature also applies here. During expansions, the nowcasting model forecasts at best are at least as good as the historical mean model, whereas during the recessionary periods, there are very substantial gains corresponding in the reduction in MSFE of about 90% relative to the benchmark model. We show how the asymmetry in the relative forecasting performance can be verified by the use of such recursive measures of relative forecast accuracy as Cumulated Sum of Squared Forecast Error Difference (CSSFED) and Recursive Relative Mean Squared Forecast Error (based on Rearranged observations) (R<sup>2</sup>MSFE(+R)). Ignoring these asymmetries results in a biased judgement of the relative forecasting performance of the competing models over a sample as a whole, as well as during economic expansions, when the forecasting accuracy of a more sophisticated model relative to naive benchmark models tends to be overstated. Hence, care needs to be exercised when ranking several models by their forecasting performance without taking into consideration various states of the economy. |
| first_indexed | 2024-03-09T05:11:26Z |
| format | Article |
| id | doaj.art-d3587906f76448e38f08a3f0ef74bc8d |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-03-09T05:11:26Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-d3587906f76448e38f08a3f0ef74bc8d2023-12-03T12:49:51ZengMDPI AGEconometrics2225-11462021-03-01911110.3390/econometrics9010011New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past? Boriss Siliverstovs0Monetary Policy Department, Bank of Latvia, K. Valdemara iela 2A, LV-1050 Riga, LatviaWe assess the forecasting performance of the nowcasting model developed at the New York FED. We show that the observation regarding a striking difference in the model’s predictive ability across business cycle phases made earlier in the literature also applies here. During expansions, the nowcasting model forecasts at best are at least as good as the historical mean model, whereas during the recessionary periods, there are very substantial gains corresponding in the reduction in MSFE of about 90% relative to the benchmark model. We show how the asymmetry in the relative forecasting performance can be verified by the use of such recursive measures of relative forecast accuracy as Cumulated Sum of Squared Forecast Error Difference (CSSFED) and Recursive Relative Mean Squared Forecast Error (based on Rearranged observations) (R<sup>2</sup>MSFE(+R)). Ignoring these asymmetries results in a biased judgement of the relative forecasting performance of the competing models over a sample as a whole, as well as during economic expansions, when the forecasting accuracy of a more sophisticated model relative to naive benchmark models tends to be overstated. Hence, care needs to be exercised when ranking several models by their forecasting performance without taking into consideration various states of the economy.https://www.mdpi.com/2225-1146/9/1/11US GDPnowcastsreal-time dataCOVID-19 |
| spellingShingle | Boriss Siliverstovs New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past? Econometrics US GDP nowcasts real-time data COVID-19 |
| title | New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past? |
| title_full | New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past? |
| title_fullStr | New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past? |
| title_full_unstemmed | New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past? |
| title_short | New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past? |
| title_sort | new york fed staff nowcasts and reality what can we learn about the future the present and the past |
| topic | US GDP nowcasts real-time data COVID-19 |
| url | https://www.mdpi.com/2225-1146/9/1/11 |
| work_keys_str_mv | AT borisssiliverstovs newyorkfedstaffnowcastsandrealitywhatcanwelearnaboutthefuturethepresentandthepast |